code: def _fwd_step_func(self, batch, model):
      data, label, loss_mask = batch
      y = model(data, torch.ones_like(data), lm_labels=label)
  
      def loss_func(output_tensor):
          output_tensor, _ = output_tensor
          lm_loss_ = output_tensor.float()
          lm_loss = (
              torch.sum(lm_loss_.view(-1) * loss_mask.reshape(-1))
              / loss_mask.sum()
          )
          averaged_loss = average_losses_across_data_parallel_group(
              [lm_loss]
          )
          if self.data_idx >= 1536:
              # NOTE (patwang): Loss cutoff might be excessively high but roughly one in five
              # unlucky random seeds do cause loss to spike to just under 8.0
              self.assertLess(averaged_loss, 8.0)
          return lm_loss, {"avg": averaged_loss}
  
      return y, loss_func
  
imports: import torch
  import unittest
  from apex.transformer.testing import global_vars
  from apex.transformer.testing.standalone_bert import bert_model_provider
  from apex.transformer.pipeline_parallel.schedules.common import _get_params_for_weight_decay_optimization, build_model
  from apex.transformer.pipeline_parallel.schedules import get_forward_backward_func
  from apex.transformer.pipeline_parallel.utils import average_losses_across_data_parallel_group, unwrap_model, setup_microbatch_calculator
  from apex.transformer.log_util import set_logging_level
  from apex.transformer import tensor_parallel, parallel_state
  from apex.transformer.enums import ModelType
  from apex.transformer._ucc_util import HAS_UCC
  from apex.transformer.testing.distributed_test_base import UccDistributedTestBase, NcclDistributedTestBase
  import logging
  from torch.testing._internal import common_utils
label_desc: Use image node_id 3 for calling the BertTestBase obj's underlying member method code with example usage: obj._fwd_step_func(batch, model) and returns: y,  loss_func, lm_loss,  dict